{"id":"https://openalex.org/W4282965679","doi":"https://doi.org/10.3390/rs14122843","title":"A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt","display_name":"A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt","publication_year":2022,"publication_date":"2022-06-14","ids":{"openalex":"https://openalex.org/W4282965679","doi":"https://doi.org/10.3390/rs14122843"},"language":"en","primary_location":{"id":"doi:10.3390/rs14122843","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14122843","pdf_url":"https://www.mdpi.com/2072-4292/14/12/2843/pdf?version=1655367429","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/12/2843/pdf?version=1655367429","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007415218","display_name":"Shahid Nawaz Khan","orcid":"https://orcid.org/0000-0003-2185-7276"},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]},{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["PK","US"],"is_corresponding":false,"raw_author_name":"Shahid Nawaz Khan","raw_affiliation_strings":["Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA","Institute of Geographical Information Systems, National University of Sciences and Technology, Islamabad 44000, Pakistan"],"affiliations":[{"raw_affiliation_string":"Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA","institution_ids":["https://openalex.org/I177156846"]},{"raw_affiliation_string":"Institute of Geographical Information Systems, National University of Sciences and Technology, Islamabad 44000, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419591","display_name":"Dapeng Li","orcid":"https://orcid.org/0000-0002-3255-6084"},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dapeng Li","raw_affiliation_strings":["Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA"],"affiliations":[{"raw_affiliation_string":"Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA","institution_ids":["https://openalex.org/I177156846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062377487","display_name":"Maitiniyazi Maimaitijiang","orcid":"https://orcid.org/0000-0001-6153-1583"},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maitiniyazi Maimaitijiang","raw_affiliation_strings":["Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA"],"affiliations":[{"raw_affiliation_string":"Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA","institution_ids":["https://openalex.org/I177156846"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100419591"],"corresponding_institution_ids":["https://openalex.org/I177156846"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":11.0215,"has_fulltext":true,"cited_by_count":72,"citation_normalized_percentile":{"value":0.98932221,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"14","issue":"12","first_page":"2843","last_page":"2843"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12310","display_name":"Crop Yield and Soil Fertility","score":0.9782999753952026,"subfield":{"id":"https://openalex.org/subfields/1102","display_name":"Agronomy and Crop Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7891008853912354},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6119301319122314},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5889939069747925},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5386578440666199},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5266610980033875},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5236972570419312},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.5224127769470215},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.48966434597969055},{"id":"https://openalex.org/keywords/crop-yield","display_name":"Crop yield","score":0.4778265953063965},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.4718515872955322},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4458313584327698},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.44401663541793823},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.42604267597198486},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41829168796539307},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34087100625038147},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3014267086982727},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.2952112555503845},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.1897124946117401},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.12586429715156555}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7891008853912354},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6119301319122314},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5889939069747925},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5386578440666199},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5266610980033875},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5236972570419312},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.5224127769470215},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.48966434597969055},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.4778265953063965},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.4718515872955322},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4458313584327698},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.44401663541793823},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.42604267597198486},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41829168796539307},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34087100625038147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3014267086982727},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.2952112555503845},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.1897124946117401},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.12586429715156555},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14122843","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14122843","pdf_url":"https://www.mdpi.com/2072-4292/14/12/2843/pdf?version=1655367429","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cc46d0fa7ae4453f964ab07037d3861d","is_oa":true,"landing_page_url":"https://doaj.org/article/cc46d0fa7ae4453f964ab07037d3861d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 12, p 2843 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/12/2843/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14122843","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 14; Issue 12; Pages: 2843","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14122843","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14122843","pdf_url":"https://www.mdpi.com/2072-4292/14/12/2843/pdf?version=1655367429","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.5,"display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310344","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4282965679.pdf","grobid_xml":"https://content.openalex.org/works/W4282965679.grobid-xml"},"referenced_works_count":92,"referenced_works":["https://openalex.org/W873297269","https://openalex.org/W1210020878","https://openalex.org/W1812582463","https://openalex.org/W1964357740","https://openalex.org/W1977987525","https://openalex.org/W1983293955","https://openalex.org/W1985469330","https://openalex.org/W1987337074","https://openalex.org/W1992227456","https://openalex.org/W1994221921","https://openalex.org/W1996944715","https://openalex.org/W2010633042","https://openalex.org/W2027249140","https://openalex.org/W2045066934","https://openalex.org/W2048697945","https://openalex.org/W2051187167","https://openalex.org/W2054054246","https://openalex.org/W2059523177","https://openalex.org/W2063757041","https://openalex.org/W2066174685","https://openalex.org/W2081099764","https://openalex.org/W2088081764","https://openalex.org/W2101234009","https://openalex.org/W2110608012","https://openalex.org/W2116375570","https://openalex.org/W2158863190","https://openalex.org/W2213612645","https://openalex.org/W2289982346","https://openalex.org/W2329720591","https://openalex.org/W2392998208","https://openalex.org/W2400450278","https://openalex.org/W2520535592","https://openalex.org/W2532686361","https://openalex.org/W2534655245","https://openalex.org/W2578887236","https://openalex.org/W2607747353","https://openalex.org/W2725897987","https://openalex.org/W2744077183","https://openalex.org/W2752605280","https://openalex.org/W2759257303","https://openalex.org/W2765879960","https://openalex.org/W2767964368","https://openalex.org/W2771569535","https://openalex.org/W2784432193","https://openalex.org/W2791172538","https://openalex.org/W2792606736","https://openalex.org/W2808964638","https://openalex.org/W2809715095","https://openalex.org/W2810045082","https://openalex.org/W2899043197","https://openalex.org/W2901788481","https://openalex.org/W2907618489","https://openalex.org/W2915536774","https://openalex.org/W2921949367","https://openalex.org/W2929349101","https://openalex.org/W2930389570","https://openalex.org/W2944794516","https://openalex.org/W2949739315","https://openalex.org/W2951616608","https://openalex.org/W2971456001","https://openalex.org/W2972529175","https://openalex.org/W2979666105","https://openalex.org/W2980732554","https://openalex.org/W2982418982","https://openalex.org/W2996041315","https://openalex.org/W2998363839","https://openalex.org/W2999658315","https://openalex.org/W3000098473","https://openalex.org/W3000584140","https://openalex.org/W3015527879","https://openalex.org/W3016661890","https://openalex.org/W3033083839","https://openalex.org/W3046135843","https://openalex.org/W3048168082","https://openalex.org/W3048765578","https://openalex.org/W3089876310","https://openalex.org/W3095728645","https://openalex.org/W3102148818","https://openalex.org/W3121715254","https://openalex.org/W3131016703","https://openalex.org/W3136164205","https://openalex.org/W3146049777","https://openalex.org/W3150635270","https://openalex.org/W3164809178","https://openalex.org/W3165822366","https://openalex.org/W3194059736","https://openalex.org/W4210400481","https://openalex.org/W4220822157","https://openalex.org/W6636552590","https://openalex.org/W6661686568","https://openalex.org/W6675354045","https://openalex.org/W6911097187"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W4385533602","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4313289487","https://openalex.org/W4321636153","https://openalex.org/W4385701552"],"abstract_inverted_index":{"Crop":[0],"yield":[1,27,50,218],"prediction":[2,51,219],"before":[3],"the":[4,39,53,57,63,95,122,125,130,138,171,182,201],"harvest":[5],"is":[6,187],"crucial":[7],"for":[8,220],"food":[9],"security,":[10],"grain":[11],"trade,":[12],"and":[13,65,89,116,147,168],"policy":[14],"making.":[15],"Previously,":[16],"several":[17],"machine":[18,69,154],"learning":[19,70,155],"methods":[20],"have":[21],"been":[22],"applied":[23],"to":[24,47,216],"predict":[25],"crop":[26,49],"using":[28,38],"different":[29,97],"types":[30,222],"of":[31,99,124,129,160,181,191,223],"variables.":[32],"In":[33],"this":[34,209],"study,":[35],"we":[36],"propose":[37],"Geographically":[40],"Weighted":[41],"Random":[42,90],"Forest":[43,91],"Regression":[44,74,79,83,87,92],"(GWRFR)":[45],"approach":[46],"improve":[48,217],"at":[52],"county":[54],"level":[55],"in":[56,208,225],"US":[58],"Corn":[59],"Belt.":[60],"We":[61,120],"trained":[62],"GWRFR":[64,126,139,172,186,197],"five":[66,132],"other":[67,131,153,175,192,221,226],"popular":[68],"algorithms":[71],"(Multiple":[72],"Linear":[73],"(MLR),":[75],"Partial":[76],"Least":[77],"Square":[78],"(PLSR),":[80],"Support":[81],"Vector":[82],"(SVR),":[84],"Decision":[85],"Tree":[86],"(DTR),":[88],"(RFR))":[93],"with":[94,127,140],"following":[96],"sets":[98],"features:":[100],"(1)":[101],"full":[102,141],"length":[103,142],"features;":[104],"(2)":[105],"vegetation":[106,165],"indices;":[107],"(3)":[108],"gross":[109],"primary":[110],"production":[111],"(GPP);":[112],"(4)":[113],"climate":[114],"data;":[115],"(5)":[117],"soil":[118,169],"data.":[119],"compared":[121],"results":[123,135],"those":[128],"models.":[133,176],"The":[134,177,205],"show":[136],"that":[137,190,196],"features":[143,161],"(R2":[144],"=":[145,149],"0.90":[146],"RMSE":[148],"0.764":[150],"MT/ha)":[151],"outperforms":[152,174],"algorithms.":[156],"For":[157],"individual":[158],"categories":[159],"such":[162],"as":[163],"GPP,":[164],"indices,":[166],"climate,":[167],"features,":[170],"also":[173,212],"Moran\u2019s":[178],"I":[179],"value":[180],"residuals":[183],"generated":[184],"by":[185],"smaller":[188],"than":[189],"models,":[193],"which":[194],"shows":[195],"can":[198,211],"better":[199],"address":[200],"spatial":[202],"non-stationarity":[203],"issue.":[204],"proposed":[206],"method":[207],"article":[210],"be":[213],"potentially":[214],"used":[215],"crops":[224],"regions.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2022-06-17T00:00:00"}